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Automated population‐based planning for whole brain radiation therapy

机译:基于人群的自动化全脑放射治疗计划

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摘要

Treatment planning for whole‐brain radiation treatment is technically a simple process, but in practice it takes valuable clinical time of repetitive and tedious tasks. This report presents a method that automatically segments the relevant target and normal tissues, and creates a treatment plan in only a few minutes after patient simulation. Segmentation of target and critical structures is performed automatically through morphological operations on the soft tissue and was validated by comparing with manual clinical segmentation using the Dice coefficient and Hausdorff distance. The treatment plan is generated by searching a database of previous cases for patients with similar anatomy. In this search, each database case is ranked in terms of similarity using a customized metric designed for sensitivity by including only geometrical changes that affect the dose distribution. The database case with the best match is automatically modified to replace relevant patient info and isocenter position while maintaining original beam and MLC settings. Fifteen patients with marginally acceptable treatment plans were used to validate the method. In each of these cases the anatomy was accurately segmented, but the beams and MLC settings led to a suboptimal treatment plan by either underdosing the brain or excessively irradiating critical normal tissues. For each case, the anatomy was automatically segmented with the proposed method, and the automated and manual segmentations were then compared. The mean Dice coefficient was 0.97, with a standard deviation of 0.008 for the brain, 0.85 ± 0.009 for the eyes, and 0.67 ± 0.11 for the lens. The mean Euclidian distance was 0.13 ± 0.13 mm for the brain, 0.27 ± 0.31 for the eye, and 2.34 ± 7.23 for the lens. Each case was then subsequently matched against a database of 70 validated treatment plans and the best matching plan (termed autoplanned), was compared retrospectively with the clinical plans in terms of brain coverage and maximum doses to critical structures. Maximum doses were reduced by a maximum of 8.37 Gy for the left eye (mean 2.08), 11.67 for the right eye (1.90) and, respectively, 25.44 (5.59) for the left lens and 24.40 (4.85) for the right lens. Time to generate the autoplan, including the segmentation, was 3 − 4 min. Automated database‐ based matching is an alternative to classical treatment planning that improves quality while providing a cost‐effective solution to planning through modifying previous validated plans to match a current patient's anatomy.PACS number: 87.55.D, 87.55.tg, 87.57.nm
机译:从技术上讲,全脑放射治疗的治疗计划是一个简单的过程,但实际上,它需要花费宝贵的临床时间来进行重复而乏味的工作。该报告提出了一种自动分割相关目标和正常组织的方法,并在模拟病人后仅几分钟内创建了治疗计划。目标和关键结构的分割是通过对软组织的形态学操作自动进行的,并且通过与使用Dice系数和Hausdorff距离的手动临床分割进行比较而得到验证。通过搜索先前病例数据库中具有相似解剖结构的患者来生成治疗计划。在此搜索中,通过使用仅针对影响剂量分布的几何变化而设计的针对敏感性的定制度量,按照相似性对每个数据库案例进行排名。具有最佳匹配的数据库案例将自动修改,以替换相关的患者信息和等角点位置,同时保持原始的波束和MLC设置。 15名具有勉强可接受的治疗计划的患者用于验证该方法。在上述每种情况下,都可以正确地分割解剖结构,但是光束和MLC设置会导致大脑剂量不足或过度照射关键正常组织,从而导致治疗计划欠佳。对于每种情况,都使用建议的方法对解剖结构进行自动分割,然后比较自动分割和手动分割。平均Dice系数为0.97,大脑的标准偏差为0.008,眼睛的标准偏差为0.85±0.009,晶状体的标准偏差为0.67±0.11。大脑的平均欧几里得距离为0.13±0.13 mm,眼睛为0.27±0.31,晶状体为2.34±7.23。然后,将每个病例与70个经过验证的治疗计划的数据库进行匹配,并就脑部覆盖率和对关键结构的最大剂量方面,将最佳匹配计划(称为自动计划)与临床计划进行回顾性比较。左眼的最大剂量最大降低了8.37 Gy(平均2.08),右眼的最大剂量降低了11.67(1.90),左眼的最大剂量分别降低了25.44(5.59),右眼的最大剂量降低了24.40(4.85)。生成自动计划(包括分段)的时间为3-4分钟。基于数据库的自动匹配是经典治疗计划的一种替代方法,该经典治疗计划可在改进质量的同时,通过修改先前经过验证的计划以匹配当前患者的解剖结构,为计划提供经济高效的解决方案.PACS编号:87.55.D,87.55.tg,87.57.nm

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